Developing a Decision Making Grid for determining proactive maintenance tactics

Abstract

Purpose The purpose of this paper is to further develop the Decision Making Grid (DMG) proposed by Ashraf Labib (e.g. Labib, 1998, 2004; Fernandez et al., 2003; Aslam-Zainudeen and Labib, 2011; Stephen and Labib, 2018; Seecharan et al., 2018) by proposing an innovative solution for determining proactive maintenance tactics based on mean time between failures (MTBF) and mean time to repair (MTTR) indicators. Design/methodology/approach First, the influence of MTTR and MTBF indicators on proactive maintenance tactics was computed. The tactics included risk-based maintenance (RBM), reliability-centered maintenance (RCM), total productive maintenance (TPM), design out maintenance (DOM), accessibility-centered maintenance (ACM) and business-centered maintenance (BCM). Then, the tactics were allocated to the cells of a DMG with MTTR and MTBF axes. The proposed approach was examined on 32 pieces of equipment of the Esfahan Steel Company and appropriate maintenance tactics were consequently determined. Findings The findings indicate that the DOM, BCM, RBM and ACM tactics with weights of 0.86, 0.94, 0.68 and 1.00 are located at the corners of the DMG, respectively. The two remaining tactics of TPM and RCM are located at the middle corners. Also, the results indicate that the share of tactics per spotted equipment in the grid as 62, 22 and 16 percent for RCM, DOM and BCM, respectively. Research limitations/implications While reactive and preventive maintenance strategies include corrective, prospective, predetermined, proactive and predictive policies, the focus of this study was merely on the tactics of proactive maintenance policy. The advantage of the developed DMG over Labib’s DMG lies in its application for equipment with the unique condition of the bathtub curve. Originality/value While the basic DMG has been mostly used regardless of the type of maintenance policies, this study provides a DMG for a specific application regarding the proactive policy. In addition, the heuristic approach proposed for the development of DMG distinguishes this study from other studies.